The only part-whole elements used in this algorithm were the patterns and the training examples.

the learning procedure and the validation procedure are generally applicable and we intend to use the method for the detection of other semantic relations such as manner, influence, and others.


Moreover, in order to achieve absolute values, the final score should be related to the number of concepts in an individual set and the number of words in the original SRH.

the results must be normalized in order to allow for evaluation, comparability and clearer interpretation of the semantic coherence scores.


The concept of lexical head is central to theories of syntax, and has often been used in designing hand-crafted history features (Collins, 1999;Charniak, 2000).

it is reasonable to suppose that the incorporation of this bias would improve performance.


A translation equivalent usually represents one and only one sense of the target word, at least in case the language-pair is those with different origins like English and Japanese.

a non-overlapping clustering algorithm, which is far simpler than overlapping clustering algorithms, is sufficient.


Repeating these procedures enables corpus-relevant but less dominant translation equivalents to be drawn up, while corpus-irrelevant ones are never drawn up.

a hierarchy of corpus-relevant senses or clusters of corpus-relevant translation equivalents is produced.


We then sampled 100 instances per target word from the Wall Street Journal corpus, and we sense-tagged them manually.

we estimated the ratios of the senses in the training corpus for each target word.


The sense-vs.-clue correlation matrix is originally used for word sense disambiguation.

our method guarantees that acquired senses can be distinguished by machines, and further it demonstrates the possibility of automatically optimizing the granularity of word senses.


However, all that matters for our purposes is that every tree in G projects under π to a tree in G with the same or higher probability, which is true because every rule in G does.


As discussed in Klein and Manning (2001b), the only source of constraint on what edges can be built where is the tags in the rules.

an edge with a label like NP→ • CC NP CC NP can essentially be built whenever (and only whenever) two CC tags are in the edge's right context, one of them being immediately to the right.


However, OCR systems may make segmentation errors and resulting subsequences may or may not be words.

a probabilistic segmentation model that accommodates word merge/split errors is necessary.


We made a closed vocabulary assumption to evaluate the effectiveness of our model when all correct words are in its lexicon.

although the language model is trained on only the training data, the words in the test set are included in the language model FSM, and treated as unseen vocabulary.


We now show the computation of some of the alignment probabilities (Equation 13) in this example (  used in our model has an index in this template library.

any operation that involves a mapping to (from) template sequences will be implemented as a mapping to (from) a sequence of these indices.


According to Over (2003), NIST spent about 3,000 man hours each in DUC 2001 and 2002 for topic and document selection, summary creation, and manual evaluation.

it would be wise to use these valuable resources, i.e.


All questions are attempted by the prover, but if the proof fails the QA system resorts to other answer extraction methods that were part of the system before the prover.

some questions are answered by the QA system without the prover, some only by the prover and some by both the non-prover system and the prover.


EM, on the other hand, fails to boost performance, and we attribute this phenomenon to the presence of redundant features in the underlying generative model.

we propose a wrapper-based feature selection method (John et al., 1994) for EM that results in performance improvements comparable to that observed with self-training.


But we chose to approach this problem from another direction.

we propose a new syntax-based algorithm to produce FSAs.


{reduced to rubble, blasted to ground}).

the squeezing operation, which implements a limited form of lexically driven alignment similar to that exploited by MSA algorithms, leads to FSAs that have a larger number of paths and paraphrases.


In our approach, all n-grams are considered as features and their importance is implicitly weighted by their contribution to perplexity.

we avoid an error prone preliminary feature selection step.


For a given x, define the transition matrix for position i as , and let * denote component-wise matrix product.

then where α i and β i the forward and backward state-cost vectors defined by we can use a forward pass to compute the α i and a backward bass to compute the β i and accumulate the feature expectations.


However, when the model is close to the maximum, the approximation becomes unstable, which is not surprising since it is based on feature independence assumptions that become invalid as the weights of interaction features move away from zero.

we disable the preconditioner after a certain number of iterations, determined from held-out data.


1 For this reason, independent human annotators consider only about 86% of the sentences in the test set to be correct with respect to comma placement (Beeferman et al., 1998).

a move from 47% to 58% is a quite substantial improvement, essentially a reduction in sentence error rate of some 30%.


It also improves the parser using a smaller set of training examples.

for the same parsing performance, it requires the human to check fewer sentences than S above-90% and the reference case of no selection (Figure 4(a)).


There is a subtle but significant difference, however, in that their goal is to reduce the total number of labeled training examples whereas we also wish to reduce the number of corrections made by the human.

our selection methods must take into account the quality of the parse produced by the teacher in addition to how different its parse is from the one produced by the student.


In fact, Turney argued that bigger # sets are worse because they narrow the estimate and as consequence can be affected by noise.

turney used only one word ³ 2 from the context, discarding the remaining words.


Yet poorly designed evaluations can waste research effort or even mislead researchers with faulty conclusions.

it is important to examine the quality of a new evaluation task to establish its reliability.


First they partition the WordNet taxonomy into a number of disjoint sets which are used as class-labels.

each concept has a single 'hypernym', and the 'affinity-score' between a word w and a class h is simply the set membership function, α(w, h) = 1 if w ∈ h and 0 otherwise.


We duplicated this algorithm but replaced inputs with (summary, text), parents and their respective children in the hierarchical domain tree.

for example, the summary is a monthly article when the text is a weekly article or a weekly article when the text is a daily one.


If there is more than one summary for a particular text (nonsummary article), the resulting extracts will vary if the summary articles are written on the same event, but are focused on different perspectives.

a summary article may be aligned with several extracts and extracts generated from a single text may align with many summaries.


Using this list of words, we find another list of words that most frequently co-occur with each of them individually.

a three-layer hierarchical domain signature can be created: level one, the domain name; level two, 10 words with the highest co-occurrences with the domain name; level three, 10 words that most frequently co-occur with level two signatures.


The use of an FLM with GPB in such a first pass, however, requires a decoder that supports such language models.

fLMs with GPB will be incorporated into GMTK (Bilmes, 2002), a general purpose graphical model toolkit for speech recognition and language processing.


In TREC 2002's main QA evaluation there were 67 different systems or variants thereof involved.

our corpus consists of 6 8 7 $ 9 A @ B C B answers.


We used the English sentence corresponding to each input Japanese sentence in the test set as the reference translation.

achieving a better score by the evaluation means that the translation results can be regarded as more adequate translations for the domain.


• Equivalent phrases extracted by HPA are not only semantically but also grammatically equivalent.

our method rarely generates ungrammatical sentences by substitution.


On the other hand, focusing on the upper phrase "get me a taxi," it contains four word links that correspond to the lower phrase "get a taxi for me", and they have the same syntactic category.

the node pair VP(4) is regarded as an equivalent phrase.


Each point denotes a source equivalent sentence set.

60.2 equivalent phrases on average were acquired, and 920 sentences on average were generated from a source set.


Therefore the Sum Product internode messages are computed as: and the probability of each WordNet node can be computed by a normalized interpolation of all of the incoming messages from the children nodes: Relevance of a WordNet Nodes Throughout our experiments with the training data, we discovered that some infrequent tail terms in the frame element lexicon that might not be filtered out by the statistical inference algorithm but still are frequently used in relevant text.

we defined the relevance metric for the WordNet nodes to achieve a larger coverage.


While Bag of Words approaches such as LSA have performed successfully on the content analysis task in domains such as Computer Literacy (Wiemer-Hastings et al., 1998), they have been demonstrated to perform poorly in causal domains such as research methods (Malatesta et al., 2002) because they base their predictions only on the words included in a text and not on the functional relationships between them.


In our evaluation we demonstrate that CarmelTC outperforms both Latent Semantic Analysis (LSA)  and Rainbow Naive Bayes (McCallum and Nigam, 1998), as well as a purely symbolic approach similar to (Furnkranz et al., 1998).

our evaluation demonstrates the advantage of combining predictions from symbolic and "bag of words" approaches for content analysis aspects of automatic essay grading.


", which we refer to as the Pumpkin Problem.

there are a total of six alternative classifications for each segment: Class 1 After the release the only force acting on the pumpkin is the downward force of gravity.


On each iteration, we randomly selected a subset of essays such that the number of text segments included in the test set were greater than 10 but less than 15 and then training Rainbow using the remaining text segments.

carmelTc uses the same set of training data, but unlike the other approaches, it uses its training data in two separate parts, namely one to train the Rainbow models it uses to produce the Rainbow prediction that is part of the vector representation it builds for each text and one to train the decision trees.


This is because for CarmelTC, the data for training Rainbow must be separate from that used to train the decision trees so the decision trees are trained from a realistic distribution of assigned Rainbow classes based on its performance on unseen data rather than on Rainbow's training data.

for CarmelTC, we also performed a 50 fold cross validation, but this time only over the set of 126 example essays not used to train the Rainbow models used by CarmelTC.


We tested LSA using as exemplars the same set of examples used as Rainbow training data, but it always performed better when using a small set of hand picked exemplars.

we present results here using only those hand picked exemplars.


We verified that LSA also performed better specifically on our task under these circumstances.

we compared each essay to each exemplar, and we counted LSA as identifying the corresponding "correct answer aspect" if the cosine value obtained by comparing the two vectors was above a threshold.


More importantly, although it is a statistical theory, it has been shown to mimic a number of aspects of human competence / performance (Landauer et al., 1998).

it appears to somehow capture and represent important components of meanings.


Next, LSA applies SVD to the Word-Document matrix, obtaining a representation of each document in a k dimensional space: crucially, k is much smaller than the dimension of the original space.

words that did not appear in certain documents now appear, as an estimate of their correlation to the meaning of those documents.


Even if our experiments with extensions to plain LSA were mostly unsuccessful, they are not sufficient to conclude that plain LSA cannot be improved.

we will pursue the following directions.


As the effect of the two actions (1a) and (2a), it is inferred that the specified location (counter in (1a), bag in (2a)) has been "emptied" of the object (fingerprints in (1a), groceries in (2a)).

a system could map both verbs wipe and remove onto the same action scheme.


Some languages such as Thai, Chinese, and Japanese have no word-boundary delimiter.

before solving homograph ambiguity, we need to identify word boundaries.


In grapheme-to-phoneme module, it may produce error pronunciations for a homograph which can be pronounced more than one way such as a word "เพลา" which can be pronounced [phlaw0] or [phe:0 la:0].

to improve an accuracy of Thai TTS, we have to focus on solving the problems of word boundary ambiguity and homograph ambiguity which can be viewed as a disambiguation task.


Many list entries contained multiple concepts while others contained none.

using the list entry as the unit for eval-uation is not sensible.


Notice that a sequence of such actions corresponds uniquely to an entity outcome (or a partition of mentions).

the problem of coreference resolution is equivalent to ranking the action sequences.


Therefore, the problem of coreference resolution is equivalent to ranking the action sequences.

in this work, a binary model 1  (1) the probability of starting an entity can be computed using the linking probabilities 1 , provided that the marginal 1 4 ¡ The number of all possible partitions of a set is given by the Bell number (Bell, 1934).


( 3)Model II: (more expressive) The major relaxation made here is in assuming a simple model of choosing entities to appear in documents.

in order to generate a document d, after we decide size(E d ) and Here, we have individual documents along with representatives, and the distribution over documents is: after we ignore the size components (they do not influence inferences).


Representatives and mentions are generated in the same way as in Model II.

a more general model for the distribution over documents is: The inference problem is the same as in Equ.


The longer monologues in our corpus tended to be more grammatically complex and included more disfluency.

alignment was more difficult, and a relatively naïve strategy, such as the baseline algorithm, was less effective.


However, once the second gesture is bound to the first reference, it is removed from the list of unassigned gestures.

if the second gesture were also bound to the second utterance, the penalty would still be relatively high.


We define m[i, j] to be the penalty of the optimal subset  | 4 ).

it is possible to find the globally optimal set of bindings, by moving from an O(n 3 ) algorithm to O(n 4 ).


But when "this" is followed by a verb (case 2), a deictic gesture is usually crucial for understanding the sentence.

the penalty for not assigning this keyword should be very high.


As a side note, it is worth mentioning that only 5% of the URLs returned by the two search engines for the entire Test set of questions overlapped.

the comparable performance accuracy was not due to the fact that the AnswerExtraction module had access to the same set of potential answers, but rather to the fact that the 10 best hits of both search engines provide similar answering options.


Second, for each pair of systems i and j with S i > S j , for any question q, system i has a better chance of responding correctly than system j, i.e., P iq > P jq .

the questions that are answered correctly by less capable systems always form a probabilistic subset of those answered correctly by more capable systems.


For instance, the standard error of estimate SE q exceeds 0.5 Logits for questions with Q q > 1 Logit.

the locations of the hardest questions are known with very poor precision only.


Table 1 indicates that this system failed (Datum = 0) on eight questions (q) where its modeled probability of success was very high (P sq > 0.98).

the misfit results from this system's failure to answer correctly questions that proved quite easy for most other systems.


In support of the robustness of the Rasch model, Figure 5 shows that the correlation between the two sets of estimates is nearly perfect (r = 0.99), indicating that the original and the "purified" questions produce essentially equivalent system evaluations.

the observed misfit had negligible effect on the system parameter estimates.


The purpose of equating is to achieve equivalent test scores across different test sets.

equating opens the door to calibrating the difficulty of questions and the performance of systems across the test sets used in different years.


In addition to showing a lower correlation (r linear = 0.82), raw scores also clearly posses a nonlinear component, and in fact the quadratic trend is highly significant (t quad = 13.10, p < .001).

in addition to being question-dependent, raw score and percentage based comparisons suffer from pronounced non-linearity.


We tried using this method in our experiments, but it produced less accurate results, as we show in Section 3.

our procedure can produce pairs consisting of a hypernym candidate and an HCS, which are denoted by Here, C 1 , • • • , C m are HCSs, and h(C i ) is a common hypernym candidate for hyponym candidates in an HCS Ci.


Another issue we will explore in future work is that for a scenario in which we label a data set from scratch, it is quite possible that we will not know how best to model the task we are labeling that data for.

it is likely in such situations that we will be able to develop better evolved models only after the data is annotated and more has been learned about the task.


In this display we have eliminated the head lexical items that appear redundantly at all the nonterminals in a head chain, instead indicating by a single number which daughter is the head.

s˜2 indicates that the head of the main clause is its second daughter, the VP, and its head is its first VP daughter.


The summary to be evaluated (a peer) is automatically split into sentences.

(the content units are of different granularity-EDUs for the model, and sentences for the peer).


Composite models were obtained by concatenating different combinations of the initial five summaries.

scores can be computed using one, two and so on up to five reference summaries.


We also calculate the probability of the best alignment according to the model.

we have the fol-lowing two feature functions: As the model is computationally expensive, we sorted the n-best list by the sentence length, and processed them from the shorter ones to the longer ones.


The training data includes about 1000 sentences, each of which normally has 1000 candidate translations with the exception of short sentences that have a smaller number of candidate translations.

we cannot use the PRank algorithm in the reranking task, since there are no global ranks or boundaries for all the samples.


In the end, the goal is to have the system make judgments similar to those made by a human with regard to ranking the coherence of an essay on four dimensions.

we created a detailed protocol for annotating the expressive quality of essay-based discourse elements in essays with regard to four aspects related to global and local essay coherence.


ESSAY (TYPED): The maximum acceleration a car can reach when towing a car behind it of equal mass will be halved.


Research in other domains (Litman et al., 2001;Batliner et al., 2003) has shown that features representing the di-alogue context can sometimes improve the accuracy of predicting negative user states, compared to the use of features computed from only the turn to be predicted.

we investigated the impact of supplementing our turn-level features in Figure 2 with the features in Figure 4, representing local and global 10 aspects of the prior dialogue, respectively.


However collecting amounts of non-native speech that are large enough to fully train speaker-independent models is a hard and often impractical task.

researchers have resorted to using smaller amounts of nonnative speech to retrain or adapt models that were originally trained on large corpora of native speech.


This can be attributed to the original difficulty of the task since even native speech contains a lot of disfluencies that make it difficult to parse.

robust parsers such as Phoenix, which are designed to be flexible enough to handle native disfluencies, can deal with some of the specificities of non-native speech.


Another explanation is that, on a narrow domain such as bus schedule information, the linguistic variance of non-native speech is much larger than that of native speech.

less data is required to accurately model native speech than non-native speech.


This could be attributed to the fact that the two features, viz., Path and Head Word that have been seen to be good discriminators of the semantically salient nodes in the syntax tree, are derived from the syntax tree.

far, in all experiments our unseen test data was selected from the same source as the training data.


For example, Ohishi indicated that difficult words and compound nouns are more often used in written language than in spoken language (Ohishi, 1970).

the applications are prone to produce unnatural speech, if their input is in written language.


We distinguish UES and SES (see Figure 1) using the occurrence probability in written and spoken language corpora.

large written and spoken corpora are necessary.


´ µ tends to be small when contains a noun, because only a reliable part of the parsed corpus is used to count ´ µ.

the value of the denominator '# of expressions in a corpus' should be changed depending on whether contains a noun or not.


Discussion It is challenging to detect the connotational difference between lexical paraphrases, and all the features were not explicitly given but estimated using the corpora which were prepared in the unsupervised manner.

we think that the accuracy of 76 % is very high.


We created our training and test sets by splitting the data for each verb into two parts: 90% for training and 10% for test.

there are 1025 sentences in the training set and 113 sentences in the test set, and each test set verb has been seen in the training set.


In our entire 1138-sentence corpus, only 13 occurrences of "LB" occur, and only one (in the training set) is related to the target verb.

we do not use the voice feature in our system.


The system somehow has to learn which phrases are translations of each other.

we use the following approach: first, we train statistical alignment models using GIZA ++ and compute the Viterbi word alignment of the training corpus.


Within the phrases, the reordering is learned during training.

there is no constraint on the reordering within the phrases.


Most of the longer phrases are seen only once in the training corpus.

pure relative frequencies overestimate the probability of those phrases.


In the following sections, we will present results on three tasks: Verbmobil, Xerox and Canadian Hansards.

we will show the corpus statistics for each of these tasks in this section.


In this section, we will investigate the effect of the monotonicity constraint.

we compute how many of the training corpus sentence pairs can be produced with the monotone phrase-based search.


To make these numbers more realistic, we use leaving-one-out.

phrases that are extracted from a specific sentence pair are not used to check its monotonicity.


So far, in machine translation research a single generally accepted criterion for the evaluation of the experimental results does not exist.


For the Verbmobil task, we have multiple references available.

on this task, we compute all the preceding criteria with respect to multiple references.


If we compare the monotone phrase-based system with the nonmonotone alignment template system, we see that the mPERs are similar.

the lexical choice of words is of the same quality.


The system loads only phrase pairs into memory if the source phrase occurs in the test corpus.

the large test corpus size for this task also affects the translation speed.


Within the phrases, there are no constraints on the reorderings.

this method is best suited for language pairs that have a similar order at the level of the phrases learned by the system.


Therefore, this method is best suited for language pairs that have a similar order at the level of the phrases learned by the system.

the translation process should require only local reorderings.


In essence, a derivation step can be viewed as the application of a rule.

compiling the set of derivation steps used in any derivation of δ A (S, T ) gives us, in a meaningful sense, all relevant rules that can be extracted from the triple (S, T, A).


Furthermore, all other frontier graph fragments can be created by composing 2 or more minimal graph fragments, as shown in Figure 8.

the entire set of frontier graph fragments (and all rules derivable from these fragments) can be computed systematically as follows: compute the set of minimal frontier graph fragments, compute the set of graph fragments resulting from composing 2 minimal frontier graph fragments, compute the set of graph fragments resulting from composing 3 minimal graph fragments, etc.


Furthermore, we conjecture that the set of rules derivable from frontier graph fragments is in fact equivalent to ρ A (S, T ).

we have boiled down the problem of extracting complex rules to the following simple problem: find the set of minimal frontier graph fragments of a given alignment graph.


Note that after computing the minimal graph fragment rooted at each node of the frontier set, every node of the alignment graph has been expanded at most once.


(However, these pronouns were possible antecedents to other pronouns, and thus were sometimes mistakenly selected as the correct antecedent.)

our results are not necessarily comparable to those of a system that deals with these difficulties (although previous work varies a fair bit on how their datasets were filtered in this regard).


The desired values for these parameters are obtained by maximizing the likelihood of the training data with respect to the model.

3 whereas L&L's RAP system uses an additive system of weights that is trained manually, the MaxEnt system learns a multiplicative system of weights automatically.


Naive Bayes In Naive Bayes modeling, a Bayesian probability distribution is estimated under a strong assumption: that all of the features are conditionally independent given the target value.

given n features x i with respect to the context x, we have: The context x is constant for each outcome y, so we only need to find: For most natural language processing scenarios, including ours, this independence assumption is almost certainly false.


In the case in which no features were used during "training" (see the first row of Table 1, columns 2 and 5), the models will produce the same probability for each possible antecedent.

these experiments reduce to using the Hobbs algorithm, which, performing at 68.23% accuracy, 6 provides a nontrivial baseline.


We considered using the MUC-6 and MUC-7 data sets, but their training sets were far too small to learn reliable co-occurrence statistics for a large set of contextual role relationships.

we opted to use the much    We adopted the MUC-6 guidelines for evaluating coreference relationships based on transitivity in anaphoric chains.


This leads to an objective function: Note that this objective function is convex (since it is the sum of two convex functions.)

there is a global maximum value for this objective function.


Thus, we get Now, we would like to find ∆ that maximizes Equation 8.

we take partial derivatives and set them to zero, remembering to also check whether a maximum occurs when δ k = 0.


The SVMs used the source-pair statistics, plus categorical source-pair information as input features.

the performance of these systems tends to support the hypothesis that source-pair information, and more specifically, source-pair similarity statistics, contains useful information for the link detection task.


The hyperbolic distribution has log-linear tails.

the class of hyperbolic distribution is an important alternative to the class of normal distributions and has been used for analyzing data from various scientific areas such as finance, though less frequently used in natural language processing.


We used 4 different prefix lengths: 8 bit, 12 bit, 16 bit, and 20 bit.

the clusters decrease in size by about a factor of 16 at each level.


While we found our treelet system more resistant to degradation at smaller phrase sizes than the phrase-based system, it nevertheless suffered significantly at very small phrase sizes.

it is also subject to practical problems of size, and again these problems are exacerbated since there are potentially an exponential number of treelets.


However, since this linguistic information is not explicitly and generatively encoded in the model, unseen adjective noun pairs may still be handled incorrectly.

having observed phrases in the past dramatically increases the chances that they will be translated correctly in the future.


For example, p(e 2 |f ) can be calculated straightforwardly using maximum likelihood estimation by counting how often the phrases e and f were aligned in the parallel corpus: There is nothing that limits us to estimating paraphrases probabilities from a single parallel corpus.

we can extend the definition of the paraphrase probability to include multiple corpora, as follows: where c is a parallel corpus from a set of parallel corpora C. multiple corpora may be used by summing over all paraphrase probabilities calculated from a single corpus (as in Equation 1) and normalized by the number of parallel corpora.


In this case translating a paraphrase would not be guaranteed to received an improved or identical Bleu score, as in the single word case.


In our scheme, the RS segment whose first word is closest to X is labeled "A"; the second-closest segment is labeled "B", etc.

segments are labeled in order of the (Koehn, 2004) penalty; the "A" segment gets the lowest penalty.


E.g., moving across a sevensegment DSH from left to right, there is an example of the seven-choice case, then one of the sixchoice case, etc.

this DSH provides three items for training the 5+-choice DT and one item 3.


Increasing the number of trees allows finer modeling of multi-segment cases while spreading the training data more thinly.

the optimal number of trees depends on the amount of training data.


With a 95% bootstrap confidence interval of ±0.7 BLEU (based on 1000-fold resampling), the resolution of these results is too coarse to draw firm conclusions.

we carried out another 1000-fold bootstrap resampling test on NIST04, this time for pairwise system comparison.


This division does not affect the ranking for the same noun, but, since we give a uniform threshold for selecting the verb pairs for distinct nouns, such normalization is desirable, as we conÞrmed in experiments using our development set.

we have deÞned our algorithm and a simpliÞed scoring function.


To report what she did, she may write "I clearly-wrote my name and also wrote my address" but will seldom write "I clearly-wrote my name and also clearly-wrote my address".

we can expect to be able to Þnd in coordinated sentences a large number of verb pairs consisting of two verbs with similar meanings.


This model takes into consideration features of words, such as whether the word is capitalized, all upper case, or all lower case, whether it is the first word of the sentence, or whether it contains digits and punctuation.

it captures the local context of the target word (i.e., the word to be classified; also referred to as TW).


Discharge summaries contain patterns that can serve as local context.

we built an SVMbased system that, given a target word (TW), would accurately predict whether the TW was part of PHI.


If we set a small threshold τ on the variance, then we can derive that at least m = a τ b features have a prior variance greater than τ .

b is proportional to the logarithm of the number of features that are assigned a variance greater than the threshold τ when a is fixed.


1 Whereas the sequence-based models are tractable and easily implemented, the more accurate fertility-based models are intractable and thus require approximation methods which are difficult to implement.

many practitioners use the complex GIZA++ software package (Och and Ney, 2003) as a black box, selecting model 4 as a good compromise between alignment quality and efficiency.


However, WSD tasks typically have very limited amounts of training data due to the fact that creating large-scale high-quality sense-tagged corpora is difficult and time-consuming.

the lack of sufficient labeled training data has become a major hurdle to improving the performance of supervised WSD.


"), and people tend to be more creative when they compose short messages such as using tables (e.g., an entire column of numbers to denote the number of minutes alloted for each presenter), bullet lists, abbreviations, and different month/day formats ("1/9" can mean January 9 or September 1), etc.

emails also contain more "human errors" such as misspellings ("day" to mean Thursday) and confusion about dates (e.g., using "tomorrow" when sending emails This section provides a concise overview of TCNL; readers are referred to (Han and Kohlhase, 2003;Han et al., 2006) for more detail.


It is well-known that perceptron training results in different weight vector solutions Initialize: wBPM = 0 for i = 1 to I; do Randomly shuffle the sequential order of samples in D Initialize: wi = 0 for t = 1 to T; dô if the data samples are presented sequentially in different orders.

random shuffles of the data and training a perceptron on each shuffle is effectively equivalent to sampling different models (w i ) in the version space.


A constituent is considered at level l + 1 only if the corresponding constituent at level l has a probability exceeding some threshold..


The conditional probability is calculated by:   1 if candidate pair is coreferent and antecedent is the semantic argument ARG0 of predicate run 0 else In our system, a set of pre-processing components including a POS tagger (Giménez & Màrquez, 2004), NP chunker (Kudoh & Matsumoto, 2000) and the Alias-I LingPipe Named Entity Recognizer 2 is applied to the text in order to identify the noun phrases, which are further taken as referring expressions (REs) to be used for instance generation.

we use automatically extracted noun phrases, rather than assuming perfect NP chunking.


The performance gap on all three datasets is quite small, which indicates the usefulness of using an encyclopedic knowledge base as a replacement for a lexical taxonomy.

of having different knowledge sources accounting for the resolution of different RE types, the best results are obtained by (1) combining features generated from different sources; (2) performing feature selection.


Current IR (Information Retrieval) techniques are based on keyword similarity measures and do not consider some features that are important for analyzing threaded discussions.

a typical IR system may return a ranked list of messages based on keyword queries even if, within the context of a discussion, this may not be useful or correct.


In our variant, before adding a new rule all possible contexts of all lengths are considered when selecting the best one.

the rule chains do not obey a strict order of expanding windows, though shorter contexts generally precede longer ones in the rule chains.


But, instead of phonemes, the rules are the emission symbols.

the letter 'c' emits the symbols (K/_, TH/_i, TH/_e, CH/_h) with a distribution of (.651, .236, .085, .028), for a perplexity of 2.534.


In this region most new rules serve to identify particular words of irregular spelling, as it is uncommon for long rules to generalize beyond a single instance.

when training a smoothed LTS rule system it is fair to ignore contexts larger than 7, as is done for example in the Festival synthesis system (Black, 1998).


It prevents the extraction of a transfer rule that would translate dankbar directly into appreciation since appreciation is aligned also to zutiefst and its f-structure would also have to be included in the transfer.

the primitive transfer rule for these predicates must be: In the second step, rules for more complex mappings are created by combining primitive transfer rules that are adjacent in the source and target fstructures.


A similar contradiction will arise if b and c 1 can make an inverted concatenation.

the algorithm will scan through the whole c as if from the empty stack.


Our work shows how to convert it back to a computationally friendly form without harming much of its expressiveness.

decoding with n-gram models can be fast and accurate, making it possible for our syntax-based system to overtake a comparable phrase-based system in BLEU score.


The plot shows that the V-values are fairly stable after a few initial cuts, and we also verified that the policies remained stable over the 20 students as well (see our prior work (Tetreault and Litman, 2006) for details of this method).

we can be sure that our baseline policy is indeed reliable for our corpus.


First, it is rare to have sub-DAs labeled in training data, and indeed this is true of the corpus (Shriberg et al., 2004) that we use.

some form of unsupervised clustering or pre-shallow-parsing of sub-DAs must be performed.


We notice that one issue of this kernel is that they limit the two paths must have the same length, otherwise the kernel similarity score is zero.

although this kernel shows non-trivial performance improvement than that of Culotta and Sorensen (2004), the constraint makes the two dependency kernels share the similar behavior: good precision but much lower recall on the ACE corpus.


Similar with the feature-based method, they also represent the tree feature as a link path between two entities.

we wonder whether their performance improvement is mainly due to the explicitly incorporation of diverse linguistic features instead of the kernel method itself.


We find the negative samples are 10 times more than the positive samples.

data imbalance and sparseness are potential problems.


A PCFG is a CFG with a probability assigned to each production.

the probabilities of the productions expanding a given non-terminal sum to one.


Note that the only way an MRF differs from a conditional random field (CRF) (Lafferty et al., 2001) is that the partition function is no longer observation dependent; we are modeling the joint probability of x and y instead of y given x.

learning an MRF is slightly harder than learning a CRF; we discuss this issue in section 4.4.


A natural consequence of this definition of distributional similarity is that many neighboring words will share the same prototypes.

distributional prototype features will encourage labels to persist, naturally giving the "sticky" effect of the domain.


We have already observed in Section 3 that, under our assumption on the start symbol S, we have We now observe that, for any A ∈ N with A = S and any t ∈ T (G), we have For each A ∈ N with A = S we can then write Once more we use relation (18), which replaced in (23) provides Notice that the linear system in (14) and (15) and the linear system in (21) and (24) are the same.

we conclude that quantities E p T f (A, t) and out G (A) are the same for each A ∈ N .


More in general, for integers d > 0 and K > 0, consider a tree sample T d,K consisting of d trees t i , 1 ≤ i ≤ d. Each t i contains k i ≥ 0 occurrences of rule S → aS and one occurrence of rule S → a.

we have In Figure 1 we plot H(p T d,K , p G,q ) in the case K d = 0.5 and in the case K d = 1.5.


10Following (Baum, 1972), one can show that p G (C) ≥ p G (C).

by iterating the growth transformation above, we are guaranteed to reach a local maximum for (8), or possibly a saddle point.


, A q are all of the occurrences of nonterminals in α, as they appear from left to right.

we can write d in the form ⇒ u j ) = 1 for each j with 0 ≤ j ≤ q, and further using the definition of p R and the inductive hypothesis, we can write As an easy corollary of Lemma 1, we have that R(G) is a consistent PCFG, as we can write 5 Consistency In this section we prove the main results of this paper, namely that all of the estimation methods discussed in Section 3 always provide consistent PCFGs.


Since G is not consistent, we must we can use Lemma 1 and write In words, every complete derivation d in D has a probability in R(G) that is strictly greater than in G. But this means , against our hypothesis.

g is consistent and p g is a probability distribution over set D(g).


We have already seen in Section 3 that the supervised MLE method is a special case of the cross-entropy minimization method.

we can also conclude that a PCFG trained with the supervised MLE method is always consistent.


Users of MT systems are generally interested in the string yield of those trees, and not the trees per se.

an algorithm that can return the -best unique strings from a packed forest would be a useful extension.


For example, texts with thousand entry pairs contain on average 70 positive labels, while documents with 200 pairs have around 20 positive labels.

we set m separately for every document by taking the average number of positive labels observed in the development data for the document size in question.


We then normalize the nugget weights (per-question) so that the maximum possible weight is one (by dividing each nugget weight by the maximum weight of that particular question).

a nugget assigned "vital" by the most assessors (not necessarily all) would receive a weight of one.


We tried to simulate a similar collection to the 9 main jour-nals+conferences in the Anthology, by considering 10 journals in genetics with a range of impact factors 3 , resulting in an in-factor of 0.17 (dropping to 0.14 if only 5 journals are considered).

our hypothesis that the Anthology is reasonably selfcontained, at least in comparison with other possible collections, was confirmed.


Also, our documents will be indexed fully automatically, further diminishing the scope of any subconscious human influence.

we believe that the suspect relationship between queries and indexing is negligible in the con-text of our work, as opposed to the Cranfield tests, and that the source-document principle is sound.


Although, this decreased the average in-factor of relevant references, it did, in fact, increase the average absolute number of relevant references in the index.

mAP increased to 0.1165, precision at 5 documents to 0.1016 and R-precision to 0.1201.


A second point for consideration follows directly from the first: our experiments were carried out on a new test collection and "different test collections have different intrinsic difficulty" (Buckley and Voorhees, 2004).

it is meaningless to compare statistics from this data (from a different domain) to those from the TREC collections, where queries and relevance judgements were collected in a different way, and where there are very many relevant documents.


We use a parameter α to control this balance.

finally, we obtain a pseudo document d with the following pseudo term count: We hypothesize that, in general, Θ d can be estimated more accurately from d rather than d itself because d contains more complete information about Θ d .


We now merge all nodes associated with the same time points.

the connection condition for two arcs depends only on the boundary time point.


While the smaller training set compared with that of verbal predicates may provide partial explanation for the degradation in performance, we believe another important reason is that the arguments for nominalized predicates do not occupy prominent syntactic positions such as the subject and object, as arguments of verbal predicates often do.

the syntactic structure represented in the parse tree does not provide as much of a clue for their detection and classification.


This is unlike most recent work on syntactic parsing based on gold-standard treebanks.

the induction of a probabilistic model for derivations is done in an unsupervised manner.


into However, Israel's answer failed to completely remove the U.S. suspicions.

among many possible paraphrases of the reference, we are interested only in those that use words appearing in the system output.


Unlike previous approaches, we are not aiming to produce any paraphrase of a given sentence since paraphrases induced from a parallel corpus do not necessarily produce a rewriting that makes a reference closer to the system output.

we focus on words that appear in the system output and aim to determine whether they can be used to rewrite a reference sentence.


The name "Buchanan" is represented in Arabic as "by-wkAnAn" and "Richard" is "rytshArd."

whichever choice the software makes for the correct value of the English substring "ch," it will choose incorrectly some significant number of times.


Furthermore, the generation probabilities lose their significance during long random walks since they get multiplied at each step.


The output of a hierarchical clustering algorithm is a tree where leaves are the documents and each node in the tree shows a cluster merging operation.


For these languages in which the words are often highly inflected as well as formed from several morphemes, even a vocabulary of 100,000 most common words would not give sufficient coverage (Kneissler and Klakow, 2001;Hirsimäki et al., 2005).

the solution to the language modeling clearly has to involve splitting of words into smaller modeling units that could then be adequately modeled.


Otherwise the acoustic models were similar cross-word triphone GMM-HMMs with MFCC features, MLLT transformation and the explicit phone duration modeling, except larger: 5100 different states and 16 GMMs per state.

the recognition speed is also slower than in Finnish, about 20 xRT (2.2GHz CPU).


For example, in agglutinative languages the words are long and contain a variable amount of morphemes.

any incorrect prefix or suffix would make the whole word incorrect.


To validate this reasoning, we performed 100 random choices of two 9-subject 4 groups, calculated the scores induced by the two groups, and computed Pearson correlation between the two lists.

for every BS text, we have a distribution of 100 coefficients, which is approximately normal.


If W is part of a taxonomy, expand its gloss, without repetitions, with words appearing in the glosses of all its super-ordinate concepts, up to the top of the hierarchy.

the expanded gloss for airplane#n#1 would contain words from the glosses of the relevant senses of aircraft , vehicle, transport, etc.


In this step, the labeled data is clamped to replenish the label sources from these labeled data.

the labeled data act like sources to push out labels through unlabeled data.


The first five features apply to pairs of NPs; the next set of features are applied individually to both of the NPs that are candidates for coreference.

we include two features each, e.g., J is PRONOUN and I is PRONOUN, indicating respectively whether the candidate anaphor and candidate antecedent are pronouns.


It is widely known that increasing the amount of training data in a generalized EBMT system eventually leads to saturation of performance, where all clustering methods perform about as well as baseline.

all methods have an operating region where they are the most useful.


Many approaches are implemented as two-way finite-state transducers (FST) in which each arc corresponds to a mapping of two elements, for example a phoneme and its phonetic realization or a morpheme and its meaning.

fST morphologies often assume morpheme-level compositionality.


Suppletion is also quite common in Georgian, requiring completely different stems for different tenses.

even for a verb whose lexical class is known, several pieces of information must be supplied to infer the complete inflectional paradigm.


For both sets of experiments, "documents" are represented by combining the ASR transcription with the AK1 and AK2 fields.

each document representation is generated completely automatically.


As discussed earlier ( §1), Charniak and Johnson (2001) have argued that speech repairs do not contribute to meaning and so there is little value in syntactically analyzing repairs or evaluating our ability to do so.

they relaxed standard PARSEVAL (Black et al., 1991) to treat EDITED constituents like punctuation: adjacent EDITED constituents are merged, and the internal structure and attachment of EDITED constituents is not evaluated.


When the labels associated with n 1 and n 2 are different, we can avoid evaluating ∆(n 1 , n 2 ) since it is 0.

we look for a node pair set Np ={ n1, n2 ∈ NT 1 × NT 2 : label(n1) = label(n2)}.


The same will be true for all nodes bearing an AM-X label.

the SSN can generalise across these two types of labels.


These systems all use (Charniak, 2000)'s parse trees both for training and testing, as well as various other information sources including sets of n-best parse trees, chunks, or named entities.

the partial trees output by these systems were merged with the parse trees returned by Charniak's parser (second line, PropBank column).


These techniques, however, do not work well for videos for two reasons: 1) most video transcripts are very short, as compared to a typical text collection; and 2) it is impractical to assume that there is a large video collection on a specific topic, due to the video production costs.

many keywords extracted from videos using traditional IR techniques are not really content-specific, and consequently, the video search results that are returned based on these keywords are generally unsatisfactory.


We use large-scale corpora, which reflect the realities of natural interaction, where limited control exists over syntax and the semantics of the utterances.

we quantify priming for the general case in the realistic setting provided by corpus based experiments.


Structural priming would predict that a rule (target) occurs more often shortly after a potential prime of the same rule than long afterwards -any repetition at great distance is seen as coincidental.

we can correlate the probability of repetition with the elapsed time (DIST) between prime and target.


One explanation for this lies in a reduced cognitive load of spontaneous, everyday conversation.

the more accessible, highly-frequent rules prime less.


Because this deterministic algorithm makes a single pass over the input string with no back-tracking, making decisions based on the parser's state and history, the order in which input tokens are considered affects the result.

we achieve additional parser diversity with the same algorithm, simply by varying the direction of parsing.


A more accurate WSD model will in turn yield yet better WDD results, as demonstrated in this paper.

further improvements in accuracy for both WSD and WDD can be expected through a bootstrapping cycle where WDD results are fed as input to the WSD process, and the resulting improved WSD model is then used to achieve better WDD results.


3  Tables 3 and 4 analyze the performance of each tagging strategy for both training regimes, kens, only 9820 of which are part of slot fillers.

most strategies could already reach an accuracy of 93% by always predicting the O class.


A minimal criterion for producing an expression that can achieve joint spatial reference is that the addressee must have perceptual accessibility to the item.

negative training examples for this experiment were selected from the time-periods that elapsed between the follower achieving perceptual access to the object (coming into the same room with it but not necessarily looking at it), but before the Locating description was spoken.


People tend to write acronyms without their defini-tion added nearby (Table 1), because acronyms are used to avoid the need to type long expressions.

there is a strong need to disambiguate acronyms in order to correctly analyze or retrieve text.


Separate D k from S l and obtain the set of training data, In the experiment, L is set to 1,000.

we have for each definition D k of A, at most 1,000 training data.


The training data used in the above experiment were only the type of snippets that contain acronyms and their definitions; there is no guarantee for documents that contain only acronyms are similar to the training data.

learning is not necessarily successful for real data.


In our proposed method, using Web pages containing Kanji and Hiragana (or Katakana) representations of the same proper names, we can learn how to read proper names with multiple readings via a state-of-the-art machine learner.

the proposed process requires no human intervention.


These aspects of XCS result in a more complete map of the state-action space than would be the case with strength-based classifier systems.

xCS often outperforms strength-based systems in sequential decision problems (Kovacs, 2000).


A fundamental component of user satisfaction is the system's prevention and repair of any miscommunication between it and the user.

our hotel booking system focuses on evolving essential slot confirmation strategies.


We model the prediction problem as a classification task as follows: given a sequence of words w i in a sentence W = {w 1 , • • • , w n } and a prosodic label vocabulary (l i L), we need to predict the best prosodic label sequence We approximate the conditional probability to be within a bounded n-gram context.

where is the word sequence and are the corresponding part-of-speech and additional syntactic information sequences.


The resulting classifier reached an accuracy of 77.45%, more than one percent net improvement over 76.28% accuracy of the model based solely on text features and not including kontrast.

in future work we plan to incorporate more acoustic and phonological features.


Non-contributions are viewed as failed attempt to show initiative.

we identify initiative conflicts as overlapping utterances that involve either a forward function and a non-contribution or two non-contributions.


We did not see a significant correlation between pitch range and the winner of initiative conflicts.

pitch does not seem to be a device for resolving initiative conflicts.


As the AMI meetings are scenario-driven, annotators are expected to find that most topics recur.

they are given a standard set of topic descriptions that can be used as labels for each identified topic segment.


Also, we expect decisionmaking conversations to take place towards the end of a topic segment.

in this study we include the following features: the label of the current topic segment, the position of the DA in a topic segment (measured in words, in seconds, and in %), the distance to the previous topic shift (both at the toplevel and sub-topic level)(measured in seconds), the duration of the current topic segment (both at the top-level and sub-topic level)(measured in seconds).


Each sample sentence in the JEIDA test set has only one question.

in the subjective evaluation using the JEIDA test set, translation errors that do not involve the pre-assigned question are ignored even if they are serious.


Therefore, in the subjective evaluation using the JEIDA test set, translation errors that do not involve the pre-assigned question are ignored even if they are serious.

translations that have serious errors that are not related to the question tend to be evaluated as being of high quality.


In our research, 23 scores, namely BLEU (Papineni et al., 2002) with maximum n-gram lengths of 1, 2, 3, and 4, NIST (NIST, 2002) with maximum n-gram lengths of 1, 2, 3, 4, and 5, GTM (Turian et al., 2003) with exponents of 1.0, 2.0, and 3.0, METEOR (exact) (Banerjee and Lavie, 2005), WER (Niessen et al., 2000), PER (Leusch et al., 2003), and ROUGE (Lin, 2004) with n-gram lengths of 1, 2, 3, and 4 and 4 variants (LCS, S * , SU * , W-1.2), were used to calculate each similarity S i .


We also found that the expression was also related to the yes/no question assigned to the test-set sentence.

we automatically generate yes/no questions in the following steps.


In their scheme, greater weight is assigned to components that are salient throughout the document.

their scheme focuses on global context while our scheme focuses on local context.


[Inference on the test data] It is intractable to marginalize out h from P (y, h|x).

similar to the Viterbi-training in the E-step, we approximate the distribution of h by argmax h P (h|X).


A twin-model is proposed for coreference resolution: a link component, modeling the coreferential relationship between an anaphor and a candidate antecedent, and a creation component modeling the possibility that a phrase is not coreferential with any candidate antecedent.

the creation model depends on all candidate antecedents and is often expensive to compute; constraints are imposed on feature forms so that features in the creation model can be efficiently computed from feature values in the link model.


With this notation, the score for a coreference out- is the product of individual scores assigned to the corresponding action sequence {a i } n i=1 , and the best coreference result is the one with the highest score:Ê Given n mentions, the number of all possible entity outcomes is the Bell Number (Bell, 1934): Exhaustive search is out of the question.

we organize hypotheses into a Bell Tree (Luo et al., 2004) and use a beam search with the following pruning strategy: first, a maximum beam size (typically 20) S is set, and we keep only the top S hypotheses; Second, a relative threshold r (we use 10 −5 ) is set to prune any hypothesis whose score divided by the maximum score falls below the threshold.


Then g 1gender ({{John}, his) is 1, while g 1gender ({Mary, sister}, his) is 0.

the gender feature for the creation model would be which means that there is at least one mention which has the same the gender of the current mention m t .


However, whereas we explicitly use a ranking-based loss function, LASO uses a binary classification loss function that labels each candidate structure as correct or incorrect.

each LASO training example contains all candidate predictions, whereas our training examples contain only the highest scoring incorrect prediction and the highest scoring correct prediction.


While our model does not assume word independence, it does ignore the relative ordering of the words in each field.

the model will fail whenever the order of words, or their proximity within a field carries a semantic meaning.


If we were to rank the items by their stationary distribution, the top list would be dominated by items from the center group in Figure 1(b).

we only use the stationary distribution to find the first item, and use a method described in the next section to rank the remaining items.


Our counts thus exclude actors who had only small roles in numerous movies.

high movie coverage roughly corresponds to ranking more prominent actors highly.


Apart from their computational efficiency, convolution kernels also implicitly correspond to some feature space.

both lines of work rely on an appropriately de-fined set of features.


Intuitively, a feature of a relation instance captures part of the attributive and/or structural properties of the relation instance graph.

it is natural to define a feature as a subgraph of the relation instance graph.


In our experiments, we consider three levels of small unit features in increasing order of their complexity.

first, we consider unigram features  = ({u, v, w}, {(u, v) trigram features consist of two connected edges and three nodes, where each node is also labeled with a single attribute.


Likewise, as it clusters objects together into sets of coreferential names, it collapses instances of relations together and potentially finds more shared instances between coreferential relations.

the clustering decisions for relations and objects mutually depend on one another.


The Gibbs sampler (Geman and Geman, 1984) is one of the simplest MCMC methods, in which transitions between states of the Markov chain result from sampling each component of the state conditioned on the current value of all other variables.

in our case, this means alternating between sampling from two distributions: , and every two steps we generate a new sample of t and θ.


Using a probabilistic argument, we show that the number of easily parsable permutations grows slowly enough that most permutations must be difficult, where by difficult we mean that the exponent in the complexity is greater than a constant factor times the rule length.

not only do there exist permutations that have complexity higher than the square root case of Satta and Peserico (2005), but in fact the probability that a randomly chosen permutation will have higher complexity approaches one as the rule length grows.


value of 1 so as not to change the probabilities computed.

an SCFG rule with n child nonterminals always results in a Markov Random Field with 2n + 2 variables and n + 1 factors, with each factor a function of exactly four variables.


The tree structure of the original recursive partition implies that the variable rises from two leaf nodes to the lowest common ancestor of both leaves, and is not contained in any higher nodes.

each node in the junction tree contains variables corresponding to the set of endpoints of the spans defined by the two subsets corresponding to its two children.


We will denote as ℓ i the number of spans of consecutive vertices from E i contained in T .

the spans counted by ℓ 1 and ℓ 2 correspond to continuous spans counted in computing the complexity of a chart parsing operation.


This is because there are 2ℓ i edges within V i at the left and right boundaries of the ℓ i spans, and at least t 2 − t 1 edges connecting the extra vertices from T 2 that have no matching vertex in T 1 .

from assumption (5) we have which in turn implies Similarly, using (6), we have That is, for T to have small edge expansion, the vertices in T 1 and T 2 must be collected into a small number of spans ℓ 1 and ℓ 2 .


Note that we can choose at most 2ℓ 1 + 2ℓ 2 ≤ t 1 • 2ε/(1 − ε) edges in total.

the number of choices of T 1 and T 2 is bounded above by For a given choice of T 1 and T 2 , for T to have small edge expansion, there must also not be too many edges that connect T 1 to vertices in V 2 − T 2 .


The graph H is connected and hence T has at least one out-going edge.

if t 1 + t 2 ≤ 1/ε, the edge-expansion of T is at least ε.


Therefore if t 1 + t 2 ≤ 1/ε, the edge-expansion of T is at least ε.

a set with edge-expansion less than ε must have t 1 + t 2 ≥ 1/ε, which, by (6), implies t 1 ≥ (1 − ε)/(2ε).


"absorb") do not combine with its first component suffix "en".

given two suffixes x and y, our method posits xy as a composite suffix if xy and x are similar in terms of the words to which they attach.


The resulting SCFG, however, can be  Figure 3: Partial word alignment for the CLANG statement and its English gloss shown in Figure 1(a) ambiguous.

a maximum-entropy model that defines the conditional probability of derivations (d) given an input sentence (e) is used for disambiguation: The feature functions, f i , are the number of times each rule is used in a derivation.


the entries for the word organization, no disambiguation page is defined; instead, the articles corresponding to different meanings of this word are connected by links labeled as "alternative meanings."

rather than using the senses listed in a disambiguation page as the sense inventory for a given ambiguous word, we chose instead to collect all the annotations available for that word in the Wikipedia pages, and then map these labels to a widely used sense inventory, namely WordNet.


Such outputs also result in a smoother graph, which in turn helps the LP process.

we found that a network with only 12 hidden units and relatively high R(θ) in Eq.


As the early TRECs have found (Voorhees and Tice, 1999), locating a passage that contains an answer is considerably easier than pinpointing the exact answer.

real-world user preferences may erode the advantage that QA has over IR techniques such as passage retrieval, e.g., (Zobel et al., 1995;Tellex et al., 2003).


It can be argued that quantitative evaluation is the single most important driver for advancing the state of the art in language processing technology today.

evaluation metrics and methodologies need to be carefully considered to insure that they provide proper guidance to researchers.


Also, to eliminate unlikely senses, each candidate sense's hypernym path is required to share at least ¢ ¡ of its nodes with nodes already in the Core Tree, where the user sets (usually between 40 and 60%).

the romantic appointment sense of date would not be considered as most of its hypernym path is not in the Core Tree.


If the proposed category system appears to require a lot of work to modify, then IAs are likely to reject it.

to evaluate Castanet's output, we recruited information architects and asked them to compare it to one other state-of-the-art approach as well as a baseline.


Participants generally did not suggest moves or promotions.

on all measures, we see Castanet outperforming the other state-of-the-art algorithms.


For all other α's, the feature is set to zero.

only α's which yield relatively good rankings can have non-zero features in the model.


In our scenario, we enjoy the luxury of plenty of labeled data that can be used to create similar instances.

given a test collection A to be clustered into L clusters, we create similar training sets by identifying the L training newsgroups whose centroids in tf/idf space (using the Okapi formula mentioned earlier) have the smallest angle to the centroid of A.


To allow a biased hyperplane, we use the usual trick of prepending a 1 element to each training example.

we require w which makes w 0 play the role of a bias term).


Each lower-level annotation y i will tend to have fewer rationales r ij , while y i itself will be more complex and hence more difficult to mark.

we expect that the overhead of collecting rationales will be less in many scenarios than the factor of 2 we measured.


In practice, one might annotate rationales for only some training documents-either when annotating a new corpus or when adding rationales post hoc to an existing corpus.

a range of options can be found between curves S2 and S1 of Figure 2.


In this setting, users were more free to express themselves than in the situation where users believed they were conversing with a machine.

we observe much longer sentences that often contain disfluencies.


This is different from deictic gesture, which is more intentionally delivered by a user.

incorporating this "unconscious" mode of modality in salience modeling requires more in-depth research on the role of eye gaze in attention prediction during multimodal human computer interaction.


However, this entity was not salient, only entities bedroom and chandelier 1 were salient.

the recognition with the S-Bigram2 model becomes worse than the baseline.


Since Equation (5) clearly has a quite similar form as Equation (4).

the PageRank algorithm can be regarded as an approximation of our model.


For the semantic orientations of phrasal expressions, however, it is impractical to manually determine the thresholds for each of the numerous adjectives.

we have to incorporate the neutral class using the Potts model.


Enlarging the set of seed words will increase the classification performance.

we automatically classify unlabeled pairs by means of an existing method and use the classified instances as seeds.


Although we could classify and use all the nouns that appeared in the training data (with an adjective which is different from the given one), we do not adopt such an alternative, because it will incorporate even non-collocating pairs such as "green+idea" into seeds, resulting in possible degradation of classification performance.

we sample unseen pairs consisting of a seen noun and a seen adjective from a corpus, classify the pairs with the latent variable model, and add them to the seed set.


The latent variable method is applicable only to instance pairs consisting of an adjective and a seen noun.

we computed the accuracy for 6586 instances using the latent variable method and obtained 80.76 %.


To cover 90% of occurrences in the training set, 227 rank sets are required.

treating a rank tuple as a single label is not a viable option for this task.


Under the Good Grief criterion, the agreement model's prediction will only be enforced when its grief outweighs that of the ranking models.

in cases where the prediction confidence (|a•x|) is relatively low, 6 the agreement model will essentially be ignored.


These messages often contain actionitems.

while importance and urgency are not equal to action-item content, an effective action-item detection system can form one prominent subcomponent in a larger prioritization system.


Since a typical user will eventually process all received mail, we assume that producing a quality ranking will more directly measure the impact on the user than accuracy or F1.

we focus on ROC curves and area under the curve (AUC) since both reflect the quality of the ranking produced.


Now, let ∆ be a uniform point-mass distribution over the shifted points andλ kNN , the output score of the kNN model.

given this definition of ∆, it is now straightforward to compute the kNN based reliability indicators: Similarly, we define variables for the SVM classifier by considering a document's locality in terms of nearby support vectors from the set of support vectors, V. To determine β i , we define it in terms of the closest support vector in V to d. Let be half the distance to the nearest point in the shift vectors are all rescaled to have the same length.


We wish to improve the rankings of the e-mails in the inbox such that action-item e-mails occur higher in the inbox.

we use the area under the curve (AUC) of an ROC curve as a measure of ranking performance.


Although Miller-Charles' experiment was carried out 25 years later than Rubenstein-Goodenough's, two sets of ratings are highly correlated (Pearson correlation coefficient=0.97).

miller-Charles ratings can be considered as a reliable benchmark for evaluating semantic similarity measures.


However, considering the high correlation between human subjects (0.9), there is still room for improvement.

we can conclude that 2500 examples are sufficient to leverage the proposed semantic similarity measure.


The pointwise mutual information (PMI) between two words x and y compares the probability of observing the two words together (their joint probability) to the probabilities of observing x and y independently (the probability of occurring together by chance) (Church and Hanks, 1991) The probabilities can be approximated by: , where C denotes frequency counts and N is the total number of words in the corpus.

: , where N can be ignored in comparisons.


Table 3 shows that the performance is generally better for word counts than for document counts.

we prefer the method that uses word counts (which is also faster in our particular setting).


First, letters should be generated from left to right, without any reordering.

the transliteration models can only execute forward sequential jumps.


When aligning blocks of letters within transliteration pairs, we expect most of them are close to the diagonal due to the monotone alignment nature.

a simple position metric is proposed for each block considering the relative positions within NE-pairs.


Because of the underlying nature of the noisy-channel model in our proposed transliteration approach in Section 2, the three base feature functions are extended to cover the directions both from target-to-source and source-totarget.

we have in total six feature functions for inferring transliteration blocks from a named entity pair.


There are always exceptional rules that need to be added to cover a large vocabulary set.


Letter and morph probabilities are maximum likelihood estimates (empirical Bayes).

of the probabilistic (or MDL) approach, the morph inventory discovered by the Morfessor Baseline algorithm is larger the more training data there is.


In order to compare the performance to the state of the art in dependency parsing, we have retrained the non-projective parser on the entire training data set for each language and evaluated it on the final test set from the CoNLL-X shared task (Buchholz and Marsi, 2006).

table 4 shows labeled attachment scores, the main evaluation metric used in the shared task, in comparison to the two highest scoring systems from the original evaluation (Mc-Donald et al., 2006;.


The design of admissible (or nearly admissible) heuristics which are both effective (close to actual completion costs) and also efficient to compute is a difficult, open problem in most domains.

most work on search has focused on non-optimal methods, such as beam search or pruning based on approximate models (Collins, 1999), though in certain cases admissible heuristics are known (Och and Ney, 2000;Zhang and Gildea, 2006).


While some point timexes are fully qualified, and thus also directly normalizable, many need to be anchored to another time in context in order to be fully normalized.

context dependence again enters the timex interpretation process, and now in two ways.


Since the generative entity grid does not take this into account, it learns that in general, the probability of any given entity appearing in a specific sentence is low.

it generates blank sentences (those without any nouns at all) with overwhelmingly high probability.


Although the modularity of this approach is appealing, the decisions made in the selection step cannot be retracted.

we cannot guarantee that selected units can be ordered in a meaningful way, and we may end up with a suboptimal output.


Since we color randomly, any single iteration is not guaranteed to find the optimal path; in a given coloring, two vertices along the optimal path may be assigned the same color, in which case the optimal path will never be selected.

the whole process is repeated multiple times, increasing the likelihood of finding an optimal path.


At the same time, the dynamic programming table at each vertex will have size 2 r (indexing on a bit vector of colors used per path), and the runtime of each trial will be proportional to this.

the running time for the necessary number of trials T r will be proportional to What r ≥ k should we choose to minimize this quantity?


In the previous approaches, beam search has been used for decoding.

it is natural to explore more sophisticated decoding techniques like the ones described in this paper.


Second language learners are learning both vocabulary and grammar concurrently, and reading materials for this population are chosen or authored according to both lexical and grammatical complexity.

the authors predict that a readability measure for texts intended for second language learners that incorporates both grammatical and lexical features could clearly outperform a measure based on only one of these two types of features.


However, the first set of features that included complex syntactic constructs led to better performance than the second set, which included only verb tenses, part of speech labels, and sentence length.

when syntactic parsing is not feasible because of corpora size, it seems that grammatical features requiring only part-ofspeech tagging and word counts may still improve readability predictions.


First, the language modeling approach can utilize all the words as they appear in the text as features, while the grammatical features were chosen and defined manually.

the LM approach can make measurements on a text for as many features as there are words in its lexicon.


where count(f, e) gives the total number of times the word f is aligned with the word e in the parallel corpus.

w(f |e) is the maximum likelihood estimation of the word translation probability of f given e. Ew(f i |ē, a) is calculated from a word alignment a between a phrase pairf = f 1 f 2 .


For example, because "Resumption of the session" was translated into "Wiederaufnahme der Sitzungsperiode" 120 times and "Wiederaufnahme der Sitzung" once, we extracted "Wiederaufnahme der Sitzungsperiode" as its translation.

we extracted 585,830 sentences for each language.


Obtaining four better scores is not statistically significant.

table 1 indicates where ">" and "∼" means that the differences of the BLEU scores of the corresponding SMt systems are statistically significant and insignificant, respectively.


When we combine two phrase-tables, the size of the combined phrase table is roughly s 2 = n i=1 f 2 i .

the relative size of the combined phrase table is roughly r = s 2 , where E(f ) = s 1 n and E(f 2 ) = s 2 n are the averages over f i and f 2 i , respectively.


If not filtered for a specific test set, the phrasetable becomes huge even for medium-sized tasks.

we store the tree structure on disk and load only the required parts into memory ondemand.


We will need similar symbols for the input graph.

we define F (j , j) as the set of source phrases of all paths from graph node j to node j, or formally: Here, the conditions ensure that the edge sequence is a proper path from node j to node j in the input graph and that the corresponding source phrase isf = f G j 1 ,n 1 , ..., f G j I ,n I .


This assumption holds for the standard phrase and word translation models.

we have to keep only the target phrase with the highest probability.


Note the exponential growth with increasing phrase length.

the naive algorithm is only applicable for very short phrases and heavily pruned CNs, as e.g.


Each equivalence class is a node in this new graph, and recombined items are the edges.

n -best extraction can now be performed on this graph.


In the latter work, the author shows that CP clearly outperforms both the naive single pass solution of severe pruning as well as the naive two-pass rescoring approach.

we focus on comparing our approach to CP.


Also, searching with Google's API and obtaining relevant documents can be time consuming, and we must limit the number of pages considered.

there is strong motivation to expand the list of units obtained from Google by automatically considering similar units.


Both inferred approaches also perform worse than  our system (Figure 2).

inferring units from relevant documents does not seem promising, as even the unrealistic approach of inferring only from known answer documents cannot achieve as high in answer-identification precision.


We used the IR system to index all names across all documents and used Indri to retrieve any name forms that matched the individual.

we were able to find references to Saddam, Hussein, and so on.


Intuitively, both algorithm 1, and algorithm 2 are linear time algorithms, which depends on the length of "common" subsequence, i.e., at most O(min(T, T')).

the overall time complexity of our method for computing a passage is O((T+T')(1+hlogm)+ min(T, T')).


Unfortunately, there is no benchmark corpus for this task.

we develop an annotated collection by following the similar tasks as TREC, CLEF, and NTCIR.


Video collections are difficult to be general-purpose since hundreds hours of videos might take tens of hundreds GB storage space.

general questions are quite difficult to be found in the video database.


Furthermore, some sentence did include the answer string but error recognized as different words.

instead of annotating the recognized transcripts, we used the corresponding video frames for evaluation because users can directly find the answers in the retrieved video clips and recognized text.


Although the same tags were used for all verbs, (namely Arg0, Arg1, ..., Arg5), these tags are meant to have a verb-specific meaning.

the use of a given argument label should be consistent across different uses of that verb, including syntactic alternations.


For example, an individual might not know whether to choose a skill stating that refers to "maintaining" a given product or "supporting" it or whether to choose a skill about maintaining a "database" or about maintaining "DB2".

it is desirable for the job search system to be able to find approximate matches, instead of only exact matches, between available individuals and open job positions.


Although there has been much work on text similarity metrics (Lin, 1998a;Corley and Mihalcea, 2005), most approaches treat texts as a bag of words and try to find shared words with certain statistical properties based on corpus frequencies.

the structural information in the text is ignored in these approaches.


Lin defined the commonality between A and B as )) , ( (  B  A  common  I where common(A, B) is a proportion that states the commonalities between A and B and where the amount of information in proposition s is The similarity between A and B is then defined as the ratio between the amount of information needed to state the commonality of A and B and the information needed to fully describe A and B: In order to compute common(A,B) and description(A,B), we use standard bag-of-words features, i.e., unigram features --the frequency of words computed from the entire corpus of the skill statements.

common(A,B) is the unigrams that both skill statements share, and description(A,B) is the union of the unigrams from both skill statements.


We simulate on the word level because generating student's dialog acts alone does not provide sufficient information for  our tutoring system to decide the next system's action.

the output of the three models is a student utterance along with the student certainty (cert, ncert).


To see this complexity result, let P(N) be the work to process N items starting with a popularity splits and let L(N) be the work to process N items starting with a lexicographic splits.

where α = 2−p, when p is the probability that the popular half contains sufficient matches.


However, by exploiting the "language of film" that is used to produce sports video, informative features can be extracted that are also easy to compute.

although we cannot easily identify a player hitting the ball, we can easily detect features that correlate with hitting: e.g., when a scene focusing on the pitching mound immediately jumps to one zooming in on the field (Figure 1).


Second, uncertainty indicates that the student perceives a possible misconception in their knowledge.

system responses to uncertain answers can address both the correctness and the perceived misconception.


The filter response F is given by The standard computation of F takes O(n) operations; however, for the special case of binary filters like the ones shown in figures 1a) and 1b), we can compute this response in constant time with some preprocessing as follows.

let I be the integral signal, where each element of I is given by this summation can be computed with two accesses to memory, after pre-computing and storing the values of I in an array.


The resulting essays are all trying to express the same "gist" content, so that any measurable differences between them must be due to differences in individual language ability and style.

the task is uniquely suited to measuring differences in linguistic behavior between populations.


We know from results of CVA and BLEU analyses that for both groups of speakers, higherscoring essays are more lexically similar to the prompts.

the lack of a trend for native speakers must be due to lack of increase in structural similarity between higher-scoring essays and the prompts.


By contrast, native speakers show no correlation between score and reading vs. lecture similarity.

there is a significant difference in the overall distribution and behavior between native and nonnative speaker populations.


They then use segment n-grams, segment position of the character being diacritized, the POS of the current segment, along with lexical features, including letter and word n-grams.

while many of the same elements are used in their and our work (word n-grams, features related to morphological analysis), the basic approach is quite different: while we have one procedure that chooses a correct analysis (including to- 18.0 5.5 7.9 2.5 Figure 1: Diacritization Results (all followed by single-choice-diac model); our best results are shown in boldface; Only-DLM-1 is the baseline; "Zitouni" is (Zitouni et al., 2006) kenization, morphological tag, and diacritization), they have a pipeline of processors.


The efficiency of finding the k shortest paths in this tree lies in the book-keeping of edges through a binary heap that allows for an implicit representation of paths.

the overall performance of the algorithm is efficient even for large k. it is feasible to use in situations where we want to generate a large number of paths, i.e.


The highest n that fit into the 16GB machine was 60 000.

this setting was used for extraction on the full 100k set.


Our algorithm was motivated by a multi-document summarization application which is mainly based on measuring the similarities and differences among text segments.

the basic requisite is to accurately measure similarities among texts.


The average dimensional ratio that CSSVD yielded the best average precision is 32.1%, which is 22.3% lower than that of SVD.

our algorithm has the advantage of being computationally inexpensive, assuming that we can find the optimal number of dimensions.


In present Statistical Machine Translation (SMT) systems, alignment is trained in a previous stage as the translation model.

alignment model parameters are not tuned in function of the translation task, but only indirectly.


In this paper, we propose a novel framework for discriminative training of alignment models with automated translation metrics as maximization criterion.

we just need a reference aligned at the sentence level instead of link labels at the word level.


Firstly, in order to estimate the bilingual n-gram model, only one monotonic segmentation of each sentence pair is performed.

long reorderings cause long and sparse tuples to be extracted.


In this example, the bilingual units s2-t2 and s3-t3 cannot be extracted because word positions s2 and s3 are embedded between links s1-t1 and s4-t1.

the link s4-t1 may introduces data sparseness in the translation model, although it may be a correct link.


Our approach, as it is, cannot be applied to a large corpus, since it requires to align the whole training corpus at each iteration.

an interesting further research would consist in determining whether the parameters for two main reasons.


We note that ILAmin is capable of declaring at most one microphone at a time as being worn by a current speaker.

construction of acoustic models for overlapped vocal activity states, described in (Laskowski and Schultz, 2006), results in states of at most 2 simultaneously vocalizing participants.


• We clustered utterances based on BIC criteria approach described in [7].

we managed to project the time stamp in the original transcript such that it can be used for training.


2 An integrated architecture for speech-input multi-target translation The classical architecture for spoken language multi-target translation involves a speech recogni-tion system in a serial architecture with m decoupled text-to-text translators.

the whole process involves m + searching stages, a first one for the speech signal transcription into the source language text string, and further m for the source language translation into the m target languages.


Nevertheless, in this paper we propose a natural way for acoustic models to be integrated in the same network.

the input speech-signal can be simultaneously decoded and translated into m target languages just in a single searching stage.


On the other hand, the multi-target approach offers a slightly smaller branching factor than each mono-target approach.

fewer paths have to be explored with the multi-target approach than with the decoupled one, which means that searching for a translation can be faster.


Using finer affixes reduces the n-gram language model span, and leads to poor performance for a fixed n-gram size.

we predefine a set of prefixes and suffixes and perform blind word segmentation.


Speech recognition (SR) errors remain as one of the main impediment factors to the wide adoption of speech technology, especially for continuous largevocabulary SR applications.

lowering word error rate is the focus of SR research which can benefit from analyzing SR errors.


The task is new to BioNLP in terms of the specific biomedical relation being sought.

we have to build annotated corpus from scratch and we are unable to use existing BioNLP shared task resources in our experiments.


For each constituent c in π it first guesses the pre-terminal of c, t(c) (t for "tag"), then the lexical head of c, h(c), and then the expansion of c into further constituents e(c).

the probability of a parse is given by the equation where l(c) is the label of c (e.g., whether it is a noun phrase NP, verb phrase, etc.)


When choosing determiners, we can train a language model on similar data, but not on other NPs in the article.

for the rest of our tests, we use tenfold cross-validation.


Church (2000) shows that a word appears with much higher probability when seen elsewhere in an article.

a rare NP might be unseen in tenfold cross-validation, but seen in leave-one-out.


These data are subjective, in free format and mostly textual, thus using them is difficult and time consuming.

how to mine the Weblog opinions automatically more effectively has attracted more and more attention (Gamon, 2005;Popescu, 2005;Chaovalit, 2005).


Changing the α tends to be a tradeoff, lowering PA when NA is improved and vice versa.

we used Harmonic − M ean by the following formula to find a proper value of "α".


The robustness of the modules will affect parsing accuracy.


While WordNet (Fellbaum, 1998) models SR, wordnets for other languages, such as the German wordnet GermaNet (Kunze, 2004), contain only few links expressing SR.

they are not well suited for estimating SR.


Thus, they are not well suited for estimating SR.

we apply the Wikipedia category graph as a knowledge source for SR measures.


However, using Wikipedia also leads to a performance drop on SS datasets, as knowledge about classical taxonomic relations is not explicitly modeled.

we combine GermaNet with Wikipedia, and yield substantial improvements over measures operating on a single knowledge source.


However, the dataset is biased towards strong classical relations, as word pairs were manually selected.

zesch and Gurevych (2006)  Semantic wordnet based measures Lesk (1986) introduced a measure (Les) based on the number of word overlaps in the textual definitions (or glosses) of two terms, where higher overlap means higher similarity.


GermaNet is better used for SS, and contains nouns, verbs, and adjectives, while Wikipedia is better used for SR between nouns.

germaNet and Wikipedia can be regarded as complementary.


In the same Mandarin broadcast program, the distribution and flow of summary sentences are relatively consistent.

compared with speech summarization on English sources, we can achieve the different finding that structural features play a key role in speech summarization for Mandarin broadcast news.


Automatic identification of subjective content often relies on word indicators, such as unigrams (Pang et al., 2002) or predetermined sentiment lexica .

the word positive in the sentence "This deal is a positive development for our company."


Our dataset also lends itself naturally to s-t Mincuts as we have two different views on the data.

the edges of a vertex (=sense) to the source/sink can be seen as the probability of a sense being subjective or objective without taking similarity to other senses into account, for example via considering only the sense gloss.


Selection of unlabeled data: Random selection of unlabeled data might hurt the performance of Mincuts, as they might not be related to any sense in our training/test data (denoted by A).

a basic principle is that the selected unlabeled senses should be related to the training/test data by WordNet relations.


This will be important when this method is applied to large resources such as the entire WordNet.

for seed set S and target sense t which is in domain D, the feature is defined as the following score: The seed set is a parameter, so we could have defined a feature reflecting similarity to the objective seed set as well.


The vector-ofgloss-words features are the most similar to ones used in previous work.

we opted to treat them as one ablation group (Gloss vector).


The bag-of-words approach tends to group together terms that can have a similar distribution of contextual terms.

terms that are topically related can appear in the same textual passages and will get high values using this model.


This prevents us from having a separate subset of the data for parameter tuning or feature selection.

we performed additional experiments to evaluate the usefulness of individual features.


We believe that words in the non-target language help contribute to a more accurate language development profile.

in our work we decided to keep these code-switched elements.


Most of the POS tagger errors involve verbs, nouns and pronouns.

while the accu- racy might not seem that low, it can still have a major impact on our approach since it involves the POS categories that are more relevant for this task.


We found the L-BFGS with L2 Gaussian regularization performs slightly better than the CG and the SGD.

we adopt the L-BFGS optimizer in this study.


The model does not support generalizations across languages, and has no way to capture phonotactic regularities within languages.

the resulting method does not scale to large phylogenies.


Even in expressing the same meaning, languages take different syntactic routes, leading to variation in part-of-speech sequences.

an effective multilingual model must accurately model common linguistic structure, yet remain flexible to the idiosyncrasies of each language.


When a complete tag lexicon is provided, our unsupervised model achieves an average accuracy of 95%, in comparison to 91% for an unsupervised monolingual Bayesian HMM and 97.4% for its supervised counterpart.

on average, the gap between unsupervised and supervised monolingual performance is cut by nearly two thirds.


However, the bilingual model explicitly joins each aligned word-pair into a single coupled state.

the state-space of these joined nodes grows exponentially in the number of languages.


In addition, we drop the simultaneity requirement.

the only constraint on elementary tree sets is the limit, d, on the derivational distance between the trees in a given set and their nearest common ancestor.


We will call the maximum number of nodes in a tree in the grammar t. Theoretically, any tree in the grammar could adjoin at any of these adjunction sites, meaning that the number of possible values for each entry in the history is bounded by the size of the grammar |G|.


, {α x } Label(α x @a) = ε Foot Axiom α x @a • , i, j, k, l, Λ α x @a • , i, j, k, l, Λ Figure 6: Axioms and inference rules for the CKY algorithm for delayed TL-MCTAG with a delay of d. lay list, giving a total number of active delay lists of O(|G| t(1+d(f −1)) ).

the complexity of the parser is O(n 6 |G| 2+t(1+d(f −1)) ).


Adjoin base: tries correspond to tree that have adjoined into a tree that has adjoined into the tree of the current item.

for each of the t trees that may have adjoined at a derivational distance of one, there are t more trees that may have adjoined at a derivational distance of two.


for each argument A ∈ V τ , we have a rule equivalence class We can see that in grammar G ′ each N ∈ B eventually ends up rewriting to one of N 's expansions β in G. There are two indirect paths, one through N b 1 and one through N b 2 .

this defines the probability of N → β in G, θ N →β , as the probability of rewriting N as β in G ′ via N b 1 and N b 2 .


Finally, the kernel score is normalized by K(s, s) • K(t, t) to discourage long sentences from being favored.

our similarity measure is a gapped, normalized string kernel, which is a more general measure than BLEU in that is considers noncontiguous substrings.


Compared to the larger Europarl subsets, the small data set is expected to produce more errors through training as there is much less relevant data for the machine learning algorithm to correctly extract useful information from.

there are more noisy entries in this small model, and therefore more entries to be removed.


This rule of thumb will be shown to be wrong as it stands; but 2 We include superlatives ('tallest'), comparatives ('taller'), and conditionals ('if') in this category because they have nondefault (i.e., non-upward entailing) properties -for instance, 'he is the tallest father' does not entail 'he is the tallest man'.

they also require special treatment when considering entailment relations.


An additional consideration is that we would like to focus on the discovery of novel or non-obvious DE operators.

for a given candidate DE operator c, we compute p F byNPI pcq: the value of F byNPI pcq that results if we discard all NPI contexts containing a DE operator on a list of 10 wellknown instances, namely, 'not ', 'n't', 'no', 'none', 'neither', 'nor', 'few', 'each', 'every', and 'without'.


One possible explanation for this is that the argument prediction model has substantially more training data for the nominals in intervals 0.45 to 1.00.

even if the nom- 6 Baseline and MLE are identical above the MLE threshold.